Outline

1. Background: COVID-19 in South Korea
2. Objectives: Understanding survival rates by age, sex, and province
3. Methods: Cox proportional hazards regression
4. Results
   1. Individual and geographic demographics
   2. Results from Cox proportional hazards model
5. Discussion

COVID-19 in Korea

• Global
  • 2,501,156 Confirmed Cases
  • 171,810 Deaths
• South Korea
  • 10,683 Confirmed Cases
  • 237 Deaths
• Why South Korea?
  • Foreign Minister (Kang Kyung-wha), Korea CDC, Prime Minister and Task Force
  • Transparency of data
  • Rapid & Free Country-Wide Testing
  • Open Surveillance and Open Data Sources

Risk Factors for Mortality

• Age (mixed evidence)
  • Important independent predictor of mortality in SARS and MERS.
  • Increased age was associated with death in patients with COVID-19.
  • Most deaths seen in those aged 40-60.
• Sex (male)
  • More deaths reported in males in both China & Italy.
  • Possible confounding with smoking and comorbidities.
• Geographic Clustering & Policies
  • Early adaptation of social distancing & closures
  • Proximity to ‘Patient 0’ and metropolitan living
  • Shincheonji Church Outbreak - 63.5% of all cases

Korean Provinces

Objectives

• Identify demographic and regional characteristics that impact survival of COVID-19 in South Korea
• Add to the novel body of literature about COVID-19 patterns

Data Source

• Retrospective cohort study
  • COVID-19 Diagnosis between Jan 20, 2020 to Apr 20, 2020
• Exclusion Criteria
  • Only including provinces with at least 1 death: Busan, Daegu, Gangwon-do, Gyeonggi-do, Gyeongsangbuk-do, Seoul, Ulsan

Analysis Plan

• All analysis conducted using R 3.6.3.

  • Analysis packages: survival, survminer
  • Visualization packages: ggplot2, leaflet, sjPlot

• Visualizations: geographic distribution, density plots, Kaplan-Meier plots

• Cox proportional hazard regression:

  • Survival days: # of days between confirmed and death or release (or 4/20, if still under observation)
  • Outcome: death
  • Covariates: age (calculated from birth year), sex, province

DAG

Statistical Analysis

The hazard function is the instantaneous risk of death at time \(t\), conditional on survival up to that time: \[h(t) = \lim_{\Delta t\rightarrow0} \frac{Pr[(t \leq T < t + \Delta t | T \geq t)]}{\Delta t}\]

• Data is right-censored

Cox proportional-hazards model for relationship of survival distribution to covariates:

\[h_{i}(t) = h_{0}(t) \exp(\alpha + \beta_{sex}x_{i,sex} + \beta_{age}x_{i, age} + \beta_{province}x_{i, province})\]

Model Assumptions: Proportional Hazards

We assume proportional hazards. This is acceptable, using the cox.zph function to test.

Model Assumptions: No Influential Cases

We assume no influential cases. This does not hold at the province level, but in certain provinces only 1 or 2 deaths were recorded.

COVID-19 Clusters

Demographic Characteristics

Density Curve by Sex

Density Curve by Age

Density Curve by Province

Cox Results

Visualization of Cox Results

K-M by Sex

K-M by Age

K-M by Province

Key Results

• In our study locations, 2.4% of those diagnosed with COVID-19 have died.

• There are statistically significant associations between sex and age and time to event.

  • There is a 8% increase (HR = 1.08, 95% CI: 1.06-1.10) in the expected hazard relative to a one-year increase in age, holding sex and province constant.
  • Men have a 233% higher (HR = 3.33, 95% CI: 1.99-5.57) expected hazard relative to women, holding age and province constant.

• There are statistically significant associations by province as well, although we caution that due to very low death counts it is difficult to draw any conclusions.

  • Specifically, Daegu has a 1030% higher (HR = 11.3, 95% CI: 3.30-38.4) expected hazard relative to Busan, holding sex and age constant.

Consistency

• Daegu’s Shincheonji Church made international news for its role in worsening the spread of COVID-19 in South Korea by reportedly hiding names of members.

• Early results from China and Italy also suggest that age and sex are risk factors for mortality. (Wu and McGoogan, 2020; Jordan, Adab, and Cheng, 2020; Livingston and Bucher, 2020)

Strengths and Limitations

Strengths

• South Korean data collection systems are very strong; contact tracing mechanisms suggest reliable data.

• To our knowledge, no survival analysis published on COVID-19 cases in South Korea.

Limitations

• Low death counts are good! But they make drawing inferences about survival from covariates challenging.

• Important unobserved variables: smoking status, prior chronic disease, secondary infection.

• Province is not a fine-enough level of geographic analysis.

Future Directions

• Statistical improvment: multilevel modeling for the province term
• Incorporate key unobserved variables: smoking status, chronic disease, secondary infection.
• Use tracking data to examine infection patterns

Questions?

References

1. Worldometer. Coronavirus Cases. Worldometer. doi:10.1101/2020.01.23.20018549V2
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3. Jordan RE, Adab P, Cheng KK. Covid-19: risk factors for severe disease and death. BMJ. 2020;368:m1198. doi:10.1136/bmj.m1198
4. Livingston E, Bucher K. Coronavirus Disease 2019 (COVID-19) in Italy. JAMA. 2020;323(14):1335-1335. doi:10.1001/jama.2020.4344
5. Opinion | Why Are South Koreans Blaming a Church for the Coronavirus Outbreak? - The New York Times. https://www.nytimes.com/2020/03/09/opinion/coronavirus-south-korea-church.html. Accessed April 22, 2020.
6. This is how South Korea contained COVID-19 | World Economic Forum. https://www.weforum.org/agenda/2020/03/south-korea-covid-19-containment-testing/. Accessed April 22, 2020.
7. Wu Z, McGoogan JM. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72314 Cases from the Chinese Center for Disease Control and Prevention. JAMA - J Am Med Assoc. 2020;323(13):1239-1242. doi:10.1001/jama.2020.2648
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